Soil Moisture Estimation Using Remote Sensing

Jeffrey Walker

Dept Civil and Env Eng

The University of Melbourne

Paul Houser

Hydrological Sciences Branch, Head

NASA Goddard Space Flight Center

http://www.civag.unimelb.edu.au/~jwalker

Importance of Soil Moisture

Early warning systems

Flood forecasting

Socio-economic activities

Agriculture management – yield forecasting, pesticides etc

Water management – irrigation

Policy planning

Drought relief

Global change

Weather and climate

Evapotranspiration

Soil Moisture vs Sea Surface Temp

Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).

The Situation

The Problem With LSMs

Same forcing and initial conditions but different predictions of soil moisture!

Importance of Soil Moisture

Soil Moisture Coverage: Veg (Mean PR)

Data Assimilation Defined

Definition 1: using data to force a model
ie. precipitation and evapotranspiration to force a LSM

Analogy: passenger giving instructions to a blindfolded driver on the M1 at peak hour

The Kalman Filter
(sequential assimilation)

Catchment-based LSM

Catchment Discretisation

Bias Reduced Forcing

Observational Data Sets

NCAR Northern Hemisphere Sea Level Pressure

01/1899 – present; 5 x 5 degrees

Climate Research Unit (University of East Anglia) Temperature and Precipitation

01/01 - 12/98; 0.5 x 0.5 degrees

Center for Climatic Research (University of Delaware) Terrestrial Temperature and Precipitation

01/50 - 12/96 ; 0.5 x 0.5 degree

Global Precipitation Climatology Project (GPCP)

01/86 - 03/95; 2.5 x 2.5 degree

Langley Eight Year Shortwave and Longwave Surface Radiation Budget

07/83 - 06/91 - in process of being extended; 2.5 x 2.5 degree

Re-Analysis Atmospheric Forcing Data Sets

ECMWF Re-analysis Advanced Global Data

4x/day, 01/79 - 12/93

1.125 degrees (Gaussian)

NCEP/NCAR Re-analysis

4x/day, 01/48 – 12/99

2.5 x 2.5 degrees

Bias Correct Using Monthly Mean Observational Data Sets

Berg, Famiglietti, Walker and Houser, AMS 2001

SMMR Soil Moisture Observations

Soil Moisture Time Series: Illinois

Animation

Slide 16

Evaluation of Assimilation

How can we evaluate the soil moisture assimilation?

Soil moisture – ideal but limited data available. United states has 19 stations in Illinois, 6 stations in Iowa and transect of 89 points in New Mexico for SMMR period.

Analysis increments – only provides a check for systematic biases.

Runoff - data available but assumes that soil moisture is the only reason for poor estimates.

Evapotranspiration – data not available and assumes that soil moisture is the only reason for poor estimates.

Precipitation forecasts – assumes soil moisture is the only reason for poor forecasts.

Other ?

Soil Moisture: Iowa

Conclusions

First known study to assimilate space-borne soil moisture measurements

Soil moisture estimate with assimilation was an improvement when compared to point measurements


	Jeffrey Walker
	Dept Civil and Env Eng
	The University of Melbourne



	Paul Houser
	Hydrological Sciences Branch, Head
	NASA Goddard Space Flight Center

	http://www.civag.unimelb.edu.au/~jwalker


	Early warning systems
		Flood forecasting
	Socio-economic activities
		Agriculture management – yield forecasting, pesticides etc
		Water management – irrigation
	Policy planning
		Drought relief
		Global change
	Weather and climate
		Evapotranspiration


	Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).


	Same forcing and initial conditions but different predictions of soil moisture!


	Definition 1: using data to force a model ie. precipitation and evapotranspiration to force a LSM
	Analogy: passenger giving instructions to a blindfolded driver on the M1 at peak hour


Observational Data Sets
	NCAR Northern Hemisphere Sea Level Pressure
		01/1899 – present; 5 x 5 degrees
	Climate Research Unit (University of East Anglia) Temperature and Precipitation
		01/01 - 12/98; 0.5 x 0.5 degrees
	Center for Climatic Research (University of Delaware) Terrestrial Temperature and Precipitation
		01/50 - 12/96 ; 0.5 x 0.5 degree
	Global Precipitation Climatology Project (GPCP)
		01/86 - 03/95; 2.5 x 2.5 degree
	Langley Eight Year Shortwave and Longwave Surface Radiation Budget
		07/83 - 06/91 - in process of being extended; 2.5 x 2.5 degree

Re-Analysis Atmospheric Forcing Data Sets
	ECMWF Re-analysis Advanced Global Data
		4x/day, 01/79 - 12/93
		1.125 degrees (Gaussian)
	NCEP/NCAR Re-analysis
		4x/day, 01/48 – 12/99
		2.5 x 2.5 degrees

Bias Correct Using Monthly Mean Observational Data Sets


Berg, Famiglietti, Walker and Houser, AMS 2001


	How can we evaluate the soil moisture assimilation?
		Soil moisture – ideal but limited data available. United states has 19 stations in Illinois, 6 stations in Iowa and transect of 89 points in New Mexico for SMMR period.
		Analysis increments – only provides a check for systematic biases.
		Runoff - data available but assumes that soil moisture is the only reason for poor estimates.
		Evapotranspiration – data not available and assumes that soil moisture is the only reason for poor estimates.
		Precipitation forecasts – assumes soil moisture is the only reason for poor forecasts.
		Other ?


	First known study to assimilate space-borne soil moisture measurements

	Soil moisture estimate with assimilation was an improvement when compared to point measurements